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The paper proposes the use of just mostly voiced speech (MVS) for speaker verification (SV). The speech is partitioned into an MVS part and a non-MVS part by a simple machine classification. SV experiments were held with a standard Gaussian mixture model (GMM) with universal background model (UBM) system and a GMM with computationally improved individual background model (IBM) system. They demonstrate...
Good speaker recognition systems should identify the speaker irrespective of what is spoken, including non-speech sounds that are often produced during natural conversations. In this work, the inclusion of breath sounds in the training phase of the speaker recognition is analyzed using the popular Gaussian mixture model-universal background model (GMM-UBM) and deep neural network (DNN) based systems...
Maximum a posteriori vector quantization (VQ-MAP) procedure adapts the mean vectors only and weights were not considered. To solve this problem,this paper proposes the improved VQ-MAP procedure which uses weighted mean vector to replace mean vector. Adaptive parameter sets in the improved VQ-MAP procedure are used as the training samples of least square support vector machines(LS-SVM) in speaker recognition...
The objective of this paper is to demonstrate the effectiveness of sparse representation techniques for speaker recognition. In this approach, each feature vector from unknown utterance is expressed as linear weighted sum of a dictionary of feature vectors belonging to many speakers. The weights associated with feature vectors in the dictionary are evaluated using orthogonal matching pursuit algorithm,...
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